Overview

Brought to you by YData

Dataset statistics

Number of variables33
Number of observations10324
Missing cells2383
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 MiB
Average record size in memory257.0 B

Variable types

Numeric7
Text13
Categorical9
DateTime3
Boolean1

Alerts

line item quantity is highly overall correlated with line item insurance (usd) and 1 other fieldsHigh correlation
line item value is highly overall correlated with line item insurance (usd) and 1 other fieldsHigh correlation
line item insurance (usd) is highly overall correlated with line item quantity and 1 other fieldsHigh correlation
pack price is highly overall correlated with brand and 1 other fieldsHigh correlation
unit price is highly overall correlated with brand and 1 other fieldsHigh correlation
brand is highly overall correlated with dosage form and 6 other fieldsHigh correlation
country is highly overall correlated with fulfill via and 1 other fieldsHigh correlation
dosage form is highly overall correlated with brand and 3 other fieldsHigh correlation
fulfill via is highly overall correlated with brand and 3 other fieldsHigh correlation
id is highly overall correlated with fulfill viaHigh correlation
product group is highly overall correlated with brand and 3 other fieldsHigh correlation
shipment mode is highly overall correlated with countryHigh correlation
sub classification is highly overall correlated with brand and 3 other fieldsHigh correlation
unit of measure (per pack) is highly overall correlated with brand and 3 other fieldsHigh correlation
vendor inco term is highly overall correlated with fulfill viaHigh correlation
managed by is highly imbalanced (97.4%) Imbalance
product group is highly imbalanced (69.9%) Imbalance
brand is highly imbalanced (62.3%) Imbalance
shipment mode has 360 (3.5%) missing values Missing
dosage has 1736 (16.8%) missing values Missing
line item insurance (usd) has 287 (2.8%) missing values Missing
unit price is highly skewed (γ1 = 40.58484939) Skewed
id has unique values Unique

Reproduction

Analysis started2024-12-30 16:45:29.391489
Analysis finished2024-12-30 16:45:55.755549
Duration26.36 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

High correlation  Unique 

Distinct10324
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51098.968
Minimum1
Maximum86823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:45:56.251854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5151.2
Q112795.75
median57540.5
Q383648.25
95-th percentile86167.85
Maximum86823
Range86822
Interquartile range (IQR)70852.5

Descriptive statistics

Standard deviation31944.332
Coefficient of variation (CV)0.62514633
Kurtosis-1.6398372
Mean51098.968
Median Absolute Deviation (MAD)27404
Skewness-0.23036671
Sum5.2754575 × 108
Variance1.0204404 × 109
MonotonicityNot monotonic
2024-12-30T16:45:56.660321image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
82565 1
 
< 0.1%
82594 1
 
< 0.1%
82595 1
 
< 0.1%
82596 1
 
< 0.1%
82597 1
 
< 0.1%
82599 1
 
< 0.1%
82600 1
 
< 0.1%
82601 1
 
< 0.1%
82602 1
 
< 0.1%
Other values (10314) 10314
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
23 1
< 0.1%
44 1
< 0.1%
45 1
< 0.1%
46 1
< 0.1%
47 1
< 0.1%
ValueCountFrequency (%)
86823 1
< 0.1%
86822 1
< 0.1%
86821 1
< 0.1%
86819 1
< 0.1%
86818 1
< 0.1%
86817 1
< 0.1%
86816 1
< 0.1%
86815 1
< 0.1%
86814 1
< 0.1%
86813 1
< 0.1%
Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:45:57.153770image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters103240
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.1%

Sample

1st row100-CI-T01
2nd row108-VN-T01
3rd row100-CI-T01
4th row108-VN-T01
5th row108-VN-T01
ValueCountFrequency (%)
116-za-t30 768
 
7.4%
104-ci-t30 729
 
7.1%
151-ng-t30 628
 
6.1%
114-ug-t30 596
 
5.8%
108-vn-t30 522
 
5.1%
106-ht-t30 450
 
4.4%
111-mz-t30 431
 
4.2%
110-zm-t30 406
 
3.9%
109-tz-t30 369
 
3.6%
107-rw-t30 340
 
3.3%
Other values (132) 5085
49.3%
2024-12-30T16:45:57.881745image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 103240
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 20648
20.0%
1 18694
18.1%
0 16409
15.9%
T 11732
11.4%
3 8596
8.3%
Z 3815
 
3.7%
G 2289
 
2.2%
6 2032
 
2.0%
N 1981
 
1.9%
4 1852
 
1.8%
Other values (25) 15192
14.7%

pq #
Text

Distinct1237
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:45:58.386741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length14
Median length9
Mean length9.9468229
Min length8

Characters and Unicode

Total characters102691
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique380 ?
Unique (%)3.7%

Sample

1st rowPre-PQ Process
2nd rowPre-PQ Process
3rd rowPre-PQ Process
4th rowPre-PQ Process
5th rowPre-PQ Process
ValueCountFrequency (%)
pre-pq 2681
 
20.6%
process 2681
 
20.6%
fpq-14942 205
 
1.6%
fpq-12522 154
 
1.2%
fpq-13973 110
 
0.8%
fpq-4537 98
 
0.8%
fpq-8840 90
 
0.7%
fpq-7175 78
 
0.6%
fpq-5303 78
 
0.6%
fpq-6262 75
 
0.6%
Other values (1228) 6755
51.9%
2024-12-30T16:45:59.199903image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 15686
15.3%
Q 10324
 
10.1%
- 10324
 
10.1%
F 7643
 
7.4%
1 6503
 
6.3%
s 5362
 
5.2%
e 5362
 
5.2%
r 5362
 
5.2%
2 3851
 
3.8%
4 3824
 
3.7%
Other values (10) 28450
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 102691
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 15686
15.3%
Q 10324
 
10.1%
- 10324
 
10.1%
F 7643
 
7.4%
1 6503
 
6.3%
s 5362
 
5.2%
e 5362
 
5.2%
r 5362
 
5.2%
2 3851
 
3.8%
4 3824
 
3.7%
Other values (10) 28450
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 102691
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 15686
15.3%
Q 10324
 
10.1%
- 10324
 
10.1%
F 7643
 
7.4%
1 6503
 
6.3%
s 5362
 
5.2%
e 5362
 
5.2%
r 5362
 
5.2%
2 3851
 
3.8%
4 3824
 
3.7%
Other values (10) 28450
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 102691
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 15686
15.3%
Q 10324
 
10.1%
- 10324
 
10.1%
F 7643
 
7.4%
1 6503
 
6.3%
s 5362
 
5.2%
e 5362
 
5.2%
r 5362
 
5.2%
2 3851
 
3.8%
4 3824
 
3.7%
Other values (10) 28450
27.7%
Distinct6233
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:45:59.739885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.0565672
Min length6

Characters and Unicode

Total characters93500
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4540 ?
Unique (%)44.0%

Sample

1st rowSCMS-4
2nd rowSCMS-13
3rd rowSCMS-20
4th rowSCMS-78
5th rowSCMS-81
ValueCountFrequency (%)
scms-199289 67
 
0.6%
scms-199283 63
 
0.6%
scms-183950 55
 
0.5%
scms-259075 38
 
0.4%
scms-215370 38
 
0.4%
scms-259079 33
 
0.3%
scms-23500 26
 
0.3%
scms-215410 26
 
0.3%
scms-259078 20
 
0.2%
scms-162440 20
 
0.2%
Other values (6223) 9938
96.3%
2024-12-30T16:46:00.509747image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 15243
16.3%
- 10324
11.0%
0 10280
11.0%
1 6433
 
6.9%
4 6052
 
6.5%
2 5714
 
6.1%
3 5482
 
5.9%
O 5404
 
5.8%
C 4920
 
5.3%
M 4920
 
5.3%
Other values (6) 18728
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 93500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 15243
16.3%
- 10324
11.0%
0 10280
11.0%
1 6433
 
6.9%
4 6052
 
6.5%
2 5714
 
6.1%
3 5482
 
5.9%
O 5404
 
5.8%
C 4920
 
5.3%
M 4920
 
5.3%
Other values (6) 18728
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 93500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 15243
16.3%
- 10324
11.0%
0 10280
11.0%
1 6433
 
6.9%
4 6052
 
6.5%
2 5714
 
6.1%
3 5482
 
5.9%
O 5404
 
5.8%
C 4920
 
5.3%
M 4920
 
5.3%
Other values (6) 18728
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 93500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 15243
16.3%
- 10324
11.0%
0 10280
11.0%
1 6433
 
6.9%
4 6052
 
6.5%
2 5714
 
6.1%
3 5482
 
5.9%
O 5404
 
5.8%
C 4920
 
5.3%
M 4920
 
5.3%
Other values (6) 18728
20.0%
Distinct7030
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:01.207286image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.6271794
Min length4

Characters and Unicode

Total characters78743
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5580 ?
Unique (%)54.0%

Sample

1st rowASN-8
2nd rowASN-85
3rd rowASN-14
4th rowASN-50
5th rowASN-55
ValueCountFrequency (%)
asn-19166 54
 
0.5%
asn-24415 38
 
0.4%
asn-23875 26
 
0.3%
asn-32138 19
 
0.2%
asn-28036 17
 
0.2%
asn-28034 17
 
0.2%
dn-304 17
 
0.2%
asn-28033 17
 
0.2%
asn-30792 17
 
0.2%
asn-1520 16
 
0.2%
Other values (7020) 10086
97.7%
2024-12-30T16:46:02.427826image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 10324
13.1%
- 10324
13.1%
2 6558
 
8.3%
1 6137
 
7.8%
3 5629
 
7.1%
D 5404
 
6.9%
A 4920
 
6.2%
S 4920
 
6.2%
4 3911
 
5.0%
7 3586
 
4.6%
Other values (5) 17030
21.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 78743
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 10324
13.1%
- 10324
13.1%
2 6558
 
8.3%
1 6137
 
7.8%
3 5629
 
7.1%
D 5404
 
6.9%
A 4920
 
6.2%
S 4920
 
6.2%
4 3911
 
5.0%
7 3586
 
4.6%
Other values (5) 17030
21.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 78743
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 10324
13.1%
- 10324
13.1%
2 6558
 
8.3%
1 6137
 
7.8%
3 5629
 
7.1%
D 5404
 
6.9%
A 4920
 
6.2%
S 4920
 
6.2%
4 3911
 
5.0%
7 3586
 
4.6%
Other values (5) 17030
21.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 78743
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 10324
13.1%
- 10324
13.1%
2 6558
 
8.3%
1 6137
 
7.8%
3 5629
 
7.1%
D 5404
 
6.9%
A 4920
 
6.2%
S 4920
 
6.2%
4 3911
 
5.0%
7 3586
 
4.6%
Other values (5) 17030
21.6%

country
Categorical

High correlation 

Distinct43
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
South Africa
1406 
Nigeria
1194 
Côte d'Ivoire
1083 
Uganda
779 
Vietnam
688 
Other values (38)
5174 

Length

Max length18
Median length12
Mean length8.4762689
Min length4

Characters and Unicode

Total characters87509
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowCôte d'Ivoire
2nd rowVietnam
3rd rowCôte d'Ivoire
4th rowVietnam
5th rowVietnam

Common Values

ValueCountFrequency (%)
South Africa 1406
13.6%
Nigeria 1194
11.6%
Côte d'Ivoire 1083
10.5%
Uganda 779
 
7.5%
Vietnam 688
 
6.7%
Zambia 683
 
6.6%
Haiti 655
 
6.3%
Mozambique 631
 
6.1%
Zimbabwe 538
 
5.2%
Tanzania 519
 
5.0%
Other values (33) 2148
20.8%

Length

2024-12-30T16:46:03.083930image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
south 1570
11.7%
africa 1406
 
10.5%
nigeria 1194
 
8.9%
côte 1083
 
8.1%
d'ivoire 1083
 
8.1%
uganda 779
 
5.8%
vietnam 688
 
5.1%
zambia 683
 
5.1%
haiti 655
 
4.9%
mozambique 631
 
4.7%
Other values (38) 3596
26.9%

Most occurring characters

ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 87509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12282
 
14.0%
i 10247
 
11.7%
e 5522
 
6.3%
o 4473
 
5.1%
n 4350
 
5.0%
t 4323
 
4.9%
r 3874
 
4.4%
3044
 
3.5%
u 2914
 
3.3%
m 2777
 
3.2%
Other values (38) 33703
38.5%

managed by
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
PMO - US
10265 
South Africa Field Office
 
57
Haiti Field Office
 
1
Ethiopia Field Office
 
1

Length

Max length25
Median length8
Mean length8.0960868
Min length8

Characters and Unicode

Total characters83584
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPMO - US
2nd rowPMO - US
3rd rowPMO - US
4th rowPMO - US
5th rowPMO - US

Common Values

ValueCountFrequency (%)
PMO - US 10265
99.4%
South Africa Field Office 57
 
0.6%
Haiti Field Office 1
 
< 0.1%
Ethiopia Field Office 1
 
< 0.1%

Length

2024-12-30T16:46:03.565402image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:04.383241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
pmo 10265
33.1%
10265
33.1%
us 10265
33.1%
field 59
 
0.2%
office 59
 
0.2%
south 57
 
0.2%
africa 57
 
0.2%
haiti 1
 
< 0.1%
ethiopia 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
P 10265
12.3%
M 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
P 10265
12.3%
M 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
P 10265
12.3%
M 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 83584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
20705
24.8%
O 10324
12.4%
S 10322
12.3%
P 10265
12.3%
M 10265
12.3%
- 10265
12.3%
U 10265
12.3%
i 179
 
0.2%
f 175
 
0.2%
e 118
 
0.1%
Other values (14) 701
 
0.8%

fulfill via
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
From RDC
5404 
Direct Drop
4920 

Length

Max length11
Median length8
Mean length9.4296784
Min length8

Characters and Unicode

Total characters97352
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDirect Drop
2nd rowDirect Drop
3rd rowDirect Drop
4th rowDirect Drop
5th rowDirect Drop

Common Values

ValueCountFrequency (%)
From RDC 5404
52.3%
Direct Drop 4920
47.7%

Length

2024-12-30T16:46:04.648194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:04.862704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
from 5404
26.2%
rdc 5404
26.2%
direct 4920
23.8%
drop 4920
23.8%

Most occurring characters

ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
o 10324
10.6%
10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
o 10324
10.6%
10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
o 10324
10.6%
10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 97352
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 15244
15.7%
D 15244
15.7%
o 10324
10.6%
10324
10.6%
F 5404
 
5.6%
m 5404
 
5.6%
R 5404
 
5.6%
C 5404
 
5.6%
i 4920
 
5.1%
e 4920
 
5.1%
Other values (3) 14760
15.2%

vendor inco term
Categorical

High correlation 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
N/A - From RDC
5404 
EXW
2778 
DDP
1443 
FCA
 
397
CIP
 
275
Other values (3)
 
27

Length

Max length14
Median length14
Mean length8.7578458
Min length3

Characters and Unicode

Total characters90416
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEXW
2nd rowEXW
3rd rowFCA
4th rowEXW
5th rowEXW

Common Values

ValueCountFrequency (%)
N/A - From RDC 5404
52.3%
EXW 2778
26.9%
DDP 1443
 
14.0%
FCA 397
 
3.8%
CIP 275
 
2.7%
DDU 15
 
0.1%
DAP 9
 
0.1%
CIF 3
 
< 0.1%

Length

2024-12-30T16:46:05.121891image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:05.375689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
n/a 5404
20.4%
5404
20.4%
from 5404
20.4%
rdc 5404
20.4%
exw 2778
10.5%
ddp 1443
 
5.4%
fca 397
 
1.5%
cip 275
 
1.0%
ddu 15
 
0.1%
dap 9
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
m 5404
 
6.0%
/ 5404
 
6.0%
R 5404
 
6.0%
o 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
m 5404
 
6.0%
/ 5404
 
6.0%
R 5404
 
6.0%
o 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
m 5404
 
6.0%
/ 5404
 
6.0%
R 5404
 
6.0%
o 5404
 
6.0%
Other values (8) 21162
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 90416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16212
17.9%
D 8329
 
9.2%
C 6079
 
6.7%
A 5810
 
6.4%
F 5804
 
6.4%
N 5404
 
6.0%
m 5404
 
6.0%
/ 5404
 
6.0%
R 5404
 
6.0%
o 5404
 
6.0%
Other values (8) 21162
23.4%

shipment mode
Categorical

High correlation  Missing 

Distinct4
Distinct (%)< 0.1%
Missing360
Missing (%)3.5%
Memory size80.8 KiB
Air
6113 
Truck
2830 
Air Charter
650 
Ocean
 
371

Length

Max length11
Median length3
Mean length4.1643918
Min length3

Characters and Unicode

Total characters41494
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAir
2nd rowAir
3rd rowAir
4th rowAir
5th rowAir

Common Values

ValueCountFrequency (%)
Air 6113
59.2%
Truck 2830
27.4%
Air Charter 650
 
6.3%
Ocean 371
 
3.6%
(Missing) 360
 
3.5%

Length

2024-12-30T16:46:05.677084image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:05.913582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
air 6763
63.7%
truck 2830
26.7%
charter 650
 
6.1%
ocean 371
 
3.5%

Most occurring characters

ValueCountFrequency (%)
r 10893
26.3%
A 6763
16.3%
i 6763
16.3%
c 3201
 
7.7%
T 2830
 
6.8%
u 2830
 
6.8%
k 2830
 
6.8%
a 1021
 
2.5%
e 1021
 
2.5%
650
 
1.6%
Other values (5) 2692
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41494
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 10893
26.3%
A 6763
16.3%
i 6763
16.3%
c 3201
 
7.7%
T 2830
 
6.8%
u 2830
 
6.8%
k 2830
 
6.8%
a 1021
 
2.5%
e 1021
 
2.5%
650
 
1.6%
Other values (5) 2692
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41494
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 10893
26.3%
A 6763
16.3%
i 6763
16.3%
c 3201
 
7.7%
T 2830
 
6.8%
u 2830
 
6.8%
k 2830
 
6.8%
a 1021
 
2.5%
e 1021
 
2.5%
650
 
1.6%
Other values (5) 2692
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41494
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 10893
26.3%
A 6763
16.3%
i 6763
16.3%
c 3201
 
7.7%
T 2830
 
6.8%
u 2830
 
6.8%
k 2830
 
6.8%
a 1021
 
2.5%
e 1021
 
2.5%
650
 
1.6%
Other values (5) 2692
 
6.5%
Distinct765
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:06.384620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length10.355579
Min length8

Characters and Unicode

Total characters106911
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)1.3%

Sample

1st rowPre-PQ Process
2nd rowPre-PQ Process
3rd rowPre-PQ Process
4th rowPre-PQ Process
5th rowPre-PQ Process
ValueCountFrequency (%)
pre-pq 2476
 
18.7%
process 2476
 
18.7%
9/11/2014 205
 
1.6%
date 205
 
1.6%
not 205
 
1.6%
captured 205
 
1.6%
7/11/2013 173
 
1.3%
4/30/2014 123
 
0.9%
11/6/2009 98
 
0.7%
11/21/2011 90
 
0.7%
Other values (758) 6954
52.6%
2024-12-30T16:46:07.180311image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15328
14.3%
/ 15286
14.3%
2 12611
11.8%
0 11326
10.6%
P 7428
 
6.9%
e 5362
 
5.0%
r 5157
 
4.8%
s 4952
 
4.6%
3 3479
 
3.3%
4 2986
 
2.8%
Other values (18) 22996
21.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 106911
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 15328
14.3%
/ 15286
14.3%
2 12611
11.8%
0 11326
10.6%
P 7428
 
6.9%
e 5362
 
5.0%
r 5157
 
4.8%
s 4952
 
4.6%
3 3479
 
3.3%
4 2986
 
2.8%
Other values (18) 22996
21.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 106911
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 15328
14.3%
/ 15286
14.3%
2 12611
11.8%
0 11326
10.6%
P 7428
 
6.9%
e 5362
 
5.0%
r 5157
 
4.8%
s 4952
 
4.6%
3 3479
 
3.3%
4 2986
 
2.8%
Other values (18) 22996
21.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 106911
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 15328
14.3%
/ 15286
14.3%
2 12611
11.8%
0 11326
10.6%
P 7428
 
6.9%
e 5362
 
5.0%
r 5157
 
4.8%
s 4952
 
4.6%
3 3479
 
3.3%
4 2986
 
2.8%
Other values (18) 22996
21.5%
Distinct897
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:07.711560image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length17
Median length14
Mean length11.849961
Min length8

Characters and Unicode

Total characters122339
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique256 ?
Unique (%)2.5%

Sample

1st rowDate Not Captured
2nd rowDate Not Captured
3rd rowDate Not Captured
4th rowDate Not Captured
5th rowDate Not Captured
ValueCountFrequency (%)
n/a 5404
19.9%
5404
19.9%
from 5404
19.9%
rdc 5404
19.9%
date 328
 
1.2%
not 328
 
1.2%
captured 328
 
1.2%
8/27/2014 80
 
0.3%
3/19/2010 78
 
0.3%
8/29/2014 76
 
0.3%
Other values (892) 4358
16.0%
2024-12-30T16:46:08.472494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16868
13.8%
/ 14588
 
11.9%
2 7921
 
6.5%
1 7602
 
6.2%
0 7063
 
5.8%
o 5732
 
4.7%
D 5732
 
4.7%
N 5732
 
4.7%
r 5732
 
4.7%
C 5732
 
4.7%
Other values (18) 39637
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122339
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16868
13.8%
/ 14588
 
11.9%
2 7921
 
6.5%
1 7602
 
6.2%
0 7063
 
5.8%
o 5732
 
4.7%
D 5732
 
4.7%
N 5732
 
4.7%
r 5732
 
4.7%
C 5732
 
4.7%
Other values (18) 39637
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122339
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16868
13.8%
/ 14588
 
11.9%
2 7921
 
6.5%
1 7602
 
6.2%
0 7063
 
5.8%
o 5732
 
4.7%
D 5732
 
4.7%
N 5732
 
4.7%
r 5732
 
4.7%
C 5732
 
4.7%
Other values (18) 39637
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122339
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16868
13.8%
/ 14588
 
11.9%
2 7921
 
6.5%
1 7602
 
6.2%
0 7063
 
5.8%
o 5732
 
4.7%
D 5732
 
4.7%
N 5732
 
4.7%
r 5732
 
4.7%
C 5732
 
4.7%
Other values (18) 39637
32.4%
Distinct2006
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Minimum2006-05-02 00:00:00
Maximum2015-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T16:46:08.816029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:46:09.223282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2093
Distinct (%)20.3%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Minimum2006-05-02 00:00:00
Maximum2015-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T16:46:09.569099image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:46:09.946967image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2042
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Minimum2006-05-02 00:00:00
Maximum2015-09-14 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-30T16:46:10.286288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:46:10.601392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

product group
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
ARV
8550 
HRDT
1728 
ANTM
 
22
ACT
 
16
MRDT
 
8

Length

Max length4
Median length3
Mean length3.1702828
Min length3

Characters and Unicode

Total characters32730
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHRDT
2nd rowARV
3rd rowHRDT
4th rowARV
5th rowARV

Common Values

ValueCountFrequency (%)
ARV 8550
82.8%
HRDT 1728
 
16.7%
ANTM 22
 
0.2%
ACT 16
 
0.2%
MRDT 8
 
0.1%

Length

2024-12-30T16:46:10.900253image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:11.238547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
arv 8550
82.8%
hrdt 1728
 
16.7%
antm 22
 
0.2%
act 16
 
0.2%
mrdt 8
 
0.1%

Most occurring characters

ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32730
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 10286
31.4%
A 8588
26.2%
V 8550
26.1%
T 1774
 
5.4%
D 1736
 
5.3%
H 1728
 
5.3%
M 30
 
0.1%
N 22
 
0.1%
C 16
 
< 0.1%

sub classification
Categorical

High correlation 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Adult
6595 
Pediatric
1955 
HIV test
1567 
HIV test - Ancillary
 
161
Malaria
 
30

Length

Max length20
Median length5
Mean length6.4494382
Min length3

Characters and Unicode

Total characters66584
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHIV test
2nd rowPediatric
3rd rowHIV test
4th rowAdult
5th rowAdult

Common Values

ValueCountFrequency (%)
Adult 6595
63.9%
Pediatric 1955
 
18.9%
HIV test 1567
 
15.2%
HIV test - Ancillary 161
 
1.6%
Malaria 30
 
0.3%
ACT 16
 
0.2%

Length

2024-12-30T16:46:11.523543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-30T16:46:11.781806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
adult 6595
53.3%
pediatric 1955
 
15.8%
hiv 1728
 
14.0%
test 1728
 
14.0%
161
 
1.3%
ancillary 161
 
1.3%
malaria 30
 
0.2%
act 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 66584
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 12006
18.0%
d 8550
12.8%
l 6947
10.4%
A 6772
10.2%
u 6595
9.9%
i 4101
 
6.2%
e 3683
 
5.5%
a 2206
 
3.3%
r 2146
 
3.2%
c 2116
 
3.2%
Other values (12) 11462
17.2%

vendor
Text

Distinct73
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:12.323055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length65
Median length13
Mean length18.53332
Min length7

Characters and Unicode

Total characters191338
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.2%

Sample

1st rowRANBAXY Fine Chemicals LTD.
2nd rowAurobindo Pharma Limited
3rd rowAbbott GmbH & Co. KG
4th rowSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)
5th rowAurobindo Pharma Limited
ValueCountFrequency (%)
scms 5404
16.5%
rdc 5404
16.5%
from 5404
16.5%
limited 1288
 
3.9%
ltd 1169
 
3.6%
orgenics 754
 
2.3%
s 717
 
2.2%
buys 715
 
2.2%
wholesaler 715
 
2.2%
laboratories 705
 
2.2%
Other values (158) 10470
32.0%
2024-12-30T16:46:13.208527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 191338
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
22491
 
11.8%
S 16965
 
8.9%
C 13420
 
7.0%
R 11481
 
6.0%
M 8653
 
4.5%
r 8112
 
4.2%
D 7485
 
3.9%
o 7434
 
3.9%
L 7380
 
3.9%
m 6798
 
3.6%
Other values (44) 81119
42.4%
Distinct184
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:13.610862image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length113
Median length79
Mean length50.144808
Min length31

Characters and Unicode

Total characters517695
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st rowHIV, Reveal G3 Rapid HIV-1 Antibody Test, 30 Tests
2nd rowNevirapine 10mg/ml, oral suspension, Bottle, 240 ml
3rd rowHIV 1/2, Determine Complete HIV Kit, 100 Tests
4th rowLamivudine 150mg, tablets, 60 Tabs
5th rowStavudine 30mg, capsules, 60 Caps
ValueCountFrequency (%)
tablets 6733
 
10.3%
tabs 6711
 
10.2%
60 4269
 
6.5%
hiv 3006
 
4.6%
30 2598
 
4.0%
tests 1616
 
2.5%
kit 1579
 
2.4%
1/2 1524
 
2.3%
disoproxil 1300
 
2.0%
fumarate 1300
 
2.0%
Other values (289) 34956
53.3%
2024-12-30T16:46:14.398529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 517695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
55268
 
10.7%
i 33699
 
6.5%
a 33528
 
6.5%
0 33249
 
6.4%
e 33073
 
6.4%
t 26685
 
5.2%
s 24970
 
4.8%
, 22576
 
4.4%
l 17483
 
3.4%
n 17399
 
3.4%
Other values (63) 219765
42.5%
Distinct86
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:14.965976image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length98
Median length60
Mean length22.15091
Min length7

Characters and Unicode

Total characters228686
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.1%

Sample

1st rowHIV, Reveal G3 Rapid HIV-1 Antibody Test
2nd rowNevirapine
3rd rowHIV 1/2, Determine Complete HIV Kit
4th rowLamivudine
5th rowStavudine
ValueCountFrequency (%)
hiv 3055
 
13.6%
kit 1579
 
7.0%
1/2 1524
 
6.8%
disoproxil 1300
 
5.8%
fumarate 1300
 
5.8%
efavirenz 1125
 
5.0%
nevirapine 877
 
3.9%
determine 799
 
3.6%
lamivudine/nevirapine/zidovudine 707
 
3.2%
lamivudine/zidovudine 689
 
3.1%
Other values (148) 9451
42.2%
2024-12-30T16:46:16.186289image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 228686
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 30297
 
13.2%
e 19499
 
8.5%
a 15535
 
6.8%
n 15469
 
6.8%
v 13084
 
5.7%
12082
 
5.3%
r 11537
 
5.0%
o 10997
 
4.8%
d 9170
 
4.0%
t 8291
 
3.6%
Other values (53) 82725
36.2%

brand
Categorical

High correlation  Imbalance 

Distinct48
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Generic
7285 
Determine
799 
Uni-Gold
 
373
Aluvia
 
250
Kaletra
 
165
Other values (43)
1452 

Length

Max length15
Median length7
Mean length7.2879698
Min length3

Characters and Unicode

Total characters75241
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowReveal
2nd rowGeneric
3rd rowDetermine
4th rowGeneric
5th rowGeneric

Common Values

ValueCountFrequency (%)
Generic 7285
70.6%
Determine 799
 
7.7%
Uni-Gold 373
 
3.6%
Aluvia 250
 
2.4%
Kaletra 165
 
1.6%
Norvir 136
 
1.3%
Stat-Pak 115
 
1.1%
Bioline 113
 
1.1%
Truvada 94
 
0.9%
Videx 84
 
0.8%
Other values (38) 910
 
8.8%

Length

2024-12-30T16:46:16.717444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
generic 7285
69.7%
determine 799
 
7.6%
uni-gold 373
 
3.6%
aluvia 250
 
2.4%
kaletra 165
 
1.6%
norvir 136
 
1.3%
videx 125
 
1.2%
stat-pak 115
 
1.1%
bioline 113
 
1.1%
truvada 94
 
0.9%
Other values (40) 995
 
9.5%

Most occurring characters

ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 75241
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 18044
24.0%
i 10118
13.4%
r 9288
12.3%
n 8958
11.9%
G 7758
10.3%
c 7463
9.9%
t 1628
 
2.2%
a 1599
 
2.1%
l 1331
 
1.8%
o 991
 
1.3%
Other values (38) 8063
10.7%

dosage
Text

Missing 

Distinct54
Distinct (%)0.6%
Missing1736
Missing (%)16.8%
Memory size80.8 KiB
2024-12-30T16:46:17.291133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.3544481
Min length2

Characters and Unicode

Total characters63160
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row10mg/ml
2nd row150mg
3rd row30mg
4th row10mg/ml
5th row200mg
ValueCountFrequency (%)
300mg 990
 
11.5%
200mg 932
 
10.9%
600mg 772
 
9.0%
150/300mg 600
 
7.0%
150/300/200mg 580
 
6.8%
10mg/ml 552
 
6.4%
150mg 431
 
5.0%
200/50mg 395
 
4.6%
300/300mg 301
 
3.5%
600/300/300mg 286
 
3.3%
Other values (44) 2749
32.0%
2024-12-30T16:46:18.349877image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 21765
34.5%
m 9548
15.1%
g 8596
 
13.6%
/ 6117
 
9.7%
3 4855
 
7.7%
2 3187
 
5.0%
1 3098
 
4.9%
5 3045
 
4.8%
6 1613
 
2.6%
l 964
 
1.5%
Other values (4) 372
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 63160
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21765
34.5%
m 9548
15.1%
g 8596
 
13.6%
/ 6117
 
9.7%
3 4855
 
7.7%
2 3187
 
5.0%
1 3098
 
4.9%
5 3045
 
4.8%
6 1613
 
2.6%
l 964
 
1.5%
Other values (4) 372
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 63160
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21765
34.5%
m 9548
15.1%
g 8596
 
13.6%
/ 6117
 
9.7%
3 4855
 
7.7%
2 3187
 
5.0%
1 3098
 
4.9%
5 3045
 
4.8%
6 1613
 
2.6%
l 964
 
1.5%
Other values (4) 372
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 63160
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21765
34.5%
m 9548
15.1%
g 8596
 
13.6%
/ 6117
 
9.7%
3 4855
 
7.7%
2 3187
 
5.0%
1 3098
 
4.9%
5 3045
 
4.8%
6 1613
 
2.6%
l 964
 
1.5%
Other values (4) 372
 
0.6%

dosage form
Categorical

High correlation 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
Tablet
3532 
Tablet - FDC
2749 
Test kit
1575 
Capsule
729 
Oral solution
727 
Other values (12)
1012 

Length

Max length34
Median length33
Mean length10.253584
Min length6

Characters and Unicode

Total characters105858
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowTest kit
2nd rowOral suspension
3rd rowTest kit
4th rowTablet
5th rowCapsule

Common Values

ValueCountFrequency (%)
Tablet 3532
34.2%
Tablet - FDC 2749
26.6%
Test kit 1575
15.3%
Capsule 729
 
7.1%
Oral solution 727
 
7.0%
Chewable/dispersible tablet - FDC 239
 
2.3%
Oral suspension 214
 
2.1%
Test kit - Ancillary 161
 
1.6%
Chewable/dispersible tablet 146
 
1.4%
Delayed-release capsules 131
 
1.3%
Other values (7) 121
 
1.2%

Length

2024-12-30T16:46:19.089167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tablet 6711
33.3%
3270
16.2%
fdc 3023
15.0%
test 1736
 
8.6%
kit 1736
 
8.6%
oral 970
 
4.8%
solution 755
 
3.7%
capsule 729
 
3.6%
chewable/dispersible 385
 
1.9%
suspension 214
 
1.1%
Other values (8) 654
 
3.2%

Most occurring characters

ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 105858
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12083
11.4%
t 11415
10.8%
l 10859
10.3%
9859
9.3%
a 9472
8.9%
T 8062
 
7.6%
b 7567
 
7.1%
s 5234
 
4.9%
C 4137
 
3.9%
i 3728
 
3.5%
Other values (22) 23442
22.1%

unit of measure (per pack)
Real number (ℝ)

High correlation 

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.990895
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:19.371329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q130
median60
Q390
95-th percentile240
Maximum1000
Range999
Interquartile range (IQR)60

Descriptive statistics

Standard deviation76.579764
Coefficient of variation (CV)0.98190646
Kurtosis36.093999
Mean77.990895
Median Absolute Deviation (MAD)30
Skewness4.3025025
Sum805178
Variance5864.4602
MonotonicityNot monotonic
2024-12-30T16:46:19.663256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
60 4121
39.9%
30 2630
25.5%
100 976
 
9.5%
240 670
 
6.5%
120 474
 
4.6%
20 470
 
4.6%
90 222
 
2.2%
300 157
 
1.5%
1 126
 
1.2%
25 114
 
1.1%
Other values (21) 364
 
3.5%
ValueCountFrequency (%)
1 126
 
1.2%
2 4
 
< 0.1%
3 8
 
0.1%
5 4
 
< 0.1%
12 2
 
< 0.1%
18 4
 
< 0.1%
20 470
 
4.6%
24 2
 
< 0.1%
25 114
 
1.1%
30 2630
25.5%
ValueCountFrequency (%)
1000 16
 
0.2%
720 5
 
< 0.1%
540 7
 
0.1%
336 39
 
0.4%
300 157
 
1.5%
270 53
 
0.5%
240 670
6.5%
200 76
 
0.7%
180 76
 
0.7%
168 3
 
< 0.1%

line item quantity
Real number (ℝ)

High correlation 

Distinct5065
Distinct (%)49.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18332.535
Minimum1
Maximum619999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:20.024586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile17
Q1408
median3000
Q317039.75
95-th percentile90951.55
Maximum619999
Range619998
Interquartile range (IQR)16631.75

Descriptive statistics

Standard deviation40035.303
Coefficient of variation (CV)2.1838389
Kurtosis40.0503
Mean18332.535
Median Absolute Deviation (MAD)2950
Skewness5.0383147
Sum1.8926509 × 108
Variance1.6028255 × 109
MonotonicityNot monotonic
2024-12-30T16:46:20.428093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 93
 
0.9%
1000 91
 
0.9%
100 87
 
0.8%
2000 73
 
0.7%
5000 69
 
0.7%
500 67
 
0.6%
20000 67
 
0.6%
3000 66
 
0.6%
3 63
 
0.6%
50000 62
 
0.6%
Other values (5055) 9586
92.9%
ValueCountFrequency (%)
1 35
0.3%
2 40
0.4%
3 63
0.6%
4 46
0.4%
5 28
0.3%
6 48
0.5%
7 27
0.3%
8 26
0.3%
9 22
 
0.2%
10 54
0.5%
ValueCountFrequency (%)
619999 1
 
< 0.1%
600906 1
 
< 0.1%
555197 1
 
< 0.1%
515000 3
< 0.1%
514526 1
 
< 0.1%
460041 1
 
< 0.1%
440000 1
 
< 0.1%
438409 1
 
< 0.1%
401961 1
 
< 0.1%
400000 2
< 0.1%

line item value
Real number (ℝ)

High correlation 

Distinct8741
Distinct (%)84.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean157650.57
Minimum0
Maximum5951990.4
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:20.787324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192.5755
Q14314.5925
median30471.465
Q3166447.14
95-th percentile702831
Maximum5951990.4
Range5951990.4
Interquartile range (IQR)162132.55

Descriptive statistics

Standard deviation345292.07
Coefficient of variation (CV)2.1902368
Kurtosis54.15243
Mean157650.57
Median Absolute Deviation (MAD)29920.465
Skewness5.8370202
Sum1.6275845 × 109
Variance1.1922661 × 1011
MonotonicityNot monotonic
2024-12-30T16:46:21.138298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 29
 
0.3%
16000 23
 
0.2%
800 18
 
0.2%
0 17
 
0.2%
14400 16
 
0.2%
3200 15
 
0.1%
244216 15
 
0.1%
120000 13
 
0.1%
160 11
 
0.1%
250 11
 
0.1%
Other values (8731) 10156
98.4%
ValueCountFrequency (%)
0 17
0.2%
0.01 1
 
< 0.1%
0.03 1
 
< 0.1%
0.12 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.25 1
 
< 0.1%
0.42 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
ValueCountFrequency (%)
5951990.4 1
< 0.1%
5768697.6 1
< 0.1%
5329891.2 1
< 0.1%
5140114.74 1
< 0.1%
4959241.98 1
< 0.1%
4278871.84 1
< 0.1%
4228629.72 1
< 0.1%
4014000 1
< 0.1%
3932880 1
< 0.1%
3904000 2
< 0.1%

pack price
Real number (ℝ)

High correlation 

Distinct1175
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.910241
Minimum0
Maximum1345.64
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:21.459041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.9
Q14.12
median9.3
Q323.5925
95-th percentile80
Maximum1345.64
Range1345.64
Interquartile range (IQR)19.4725

Descriptive statistics

Standard deviation45.609223
Coefficient of variation (CV)2.0816395
Kurtosis293.1762
Mean21.910241
Median Absolute Deviation (MAD)6.57
Skewness12.988432
Sum226201.33
Variance2080.2012
MonotonicityNot monotonic
2024-12-30T16:46:21.803217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32 368
 
3.6%
80 307
 
3.0%
89 183
 
1.8%
11.22 139
 
1.3%
20 110
 
1.1%
1.95 91
 
0.9%
8.76 91
 
0.9%
2.44 90
 
0.9%
2.1 89
 
0.9%
2.26 88
 
0.9%
Other values (1165) 8768
84.9%
ValueCountFrequency (%)
0 18
 
0.2%
0.01 85
0.8%
0.39 2
 
< 0.1%
0.7 2
 
< 0.1%
0.9 4
 
< 0.1%
1.1 2
 
< 0.1%
1.14 2
 
< 0.1%
1.17 1
 
< 0.1%
1.2 1
 
< 0.1%
1.21 1
 
< 0.1%
ValueCountFrequency (%)
1345.64 1
 
< 0.1%
1250 1
 
< 0.1%
1242.53 3
 
< 0.1%
750.29 1
 
< 0.1%
700 1
 
< 0.1%
400 9
 
0.1%
350 39
0.4%
308.17 3
 
< 0.1%
306.88 3
 
< 0.1%
301.53 1
 
< 0.1%

unit price
Real number (ℝ)

High correlation  Skewed 

Distinct183
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61170089
Minimum0
Maximum238.65
Zeros103
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:22.188388image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.08
median0.16
Q30.47
95-th percentile1.6
Maximum238.65
Range238.65
Interquartile range (IQR)0.39

Descriptive statistics

Standard deviation3.2758077
Coefficient of variation (CV)5.3552443
Kurtosis2725.9603
Mean0.61170089
Median Absolute Deviation (MAD)0.12
Skewness40.584849
Sum6315.2
Variance10.730916
MonotonicityNot monotonic
2024-12-30T16:46:22.509821image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.04 713
 
6.9%
0.01 492
 
4.8%
0.12 464
 
4.5%
0.14 444
 
4.3%
0.8 411
 
4.0%
0.11 400
 
3.9%
1.6 368
 
3.6%
0.05 343
 
3.3%
0.16 343
 
3.3%
0.19 321
 
3.1%
Other values (173) 6025
58.4%
ValueCountFrequency (%)
0 103
 
1.0%
0.01 492
4.8%
0.02 140
 
1.4%
0.03 250
 
2.4%
0.04 713
6.9%
0.05 343
3.3%
0.06 274
 
2.7%
0.07 248
 
2.4%
0.08 146
 
1.4%
0.09 154
 
1.5%
ValueCountFrequency (%)
238.65 1
 
< 0.1%
41.68 1
 
< 0.1%
37.5 2
 
< 0.1%
30 1
 
< 0.1%
26.91 1
 
< 0.1%
25 4
 
< 0.1%
24.85 3
 
< 0.1%
24.5 46
0.4%
23 23
0.2%
17.12 3
 
< 0.1%
Distinct88
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:23.074410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length72
Median length37
Mean length25.039132
Min length5

Characters and Unicode

Total characters258504
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.2%

Sample

1st rowRanbaxy Fine Chemicals LTD
2nd rowAurobindo Unit III, India
3rd rowABBVIE GmbH & Co.KG Wiesbaden
4th rowRanbaxy, Paonta Shahib, India
5th rowAurobindo Unit III, India
ValueCountFrequency (%)
india 4678
 
11.9%
unit 4197
 
10.7%
iii 4041
 
10.3%
aurobindo 3283
 
8.4%
mylan 1438
 
3.7%
formerly 1415
 
3.6%
matrix 1415
 
3.6%
nashik 1415
 
3.6%
in 1047
 
2.7%
hetero 913
 
2.3%
Other values (201) 15396
39.2%
2024-12-30T16:46:23.973732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 258504
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28937
 
11.2%
i 20789
 
8.0%
I 19607
 
7.6%
a 18921
 
7.3%
n 18236
 
7.1%
r 13636
 
5.3%
o 12958
 
5.0%
d 12314
 
4.8%
e 10570
 
4.1%
t 10195
 
3.9%
Other values (59) 92341
35.7%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.2 KiB
True
7030 
False
3294 
ValueCountFrequency (%)
True 7030
68.1%
False 3294
31.9%
2024-12-30T16:46:24.265606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct4688
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:24.773244image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length26
Median length24
Mean length11.359163
Min length1

Characters and Unicode

Total characters117272
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3014 ?
Unique (%)29.2%

Sample

1st row13
2nd row358
3rd row171
4th row1855
5th row7590
ValueCountFrequency (%)
see 2445
 
13.4%
weight 1507
 
8.3%
separately 1507
 
8.3%
captured 1507
 
8.3%
2 29
 
0.2%
6 26
 
0.1%
1 23
 
0.1%
5 20
 
0.1%
60 20
 
0.1%
4 19
 
0.1%
Other values (5980) 11125
61.0%
2024-12-30T16:46:25.678030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 10918
 
9.3%
7904
 
6.7%
1 6455
 
5.5%
2 5833
 
5.0%
3 5113
 
4.4%
8 4534
 
3.9%
t 4521
 
3.9%
a 4521
 
3.9%
S 4460
 
3.8%
D 4382
 
3.7%
Other values (25) 58631
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 117272
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10918
 
9.3%
7904
 
6.7%
1 6455
 
5.5%
2 5833
 
5.0%
3 5113
 
4.4%
8 4534
 
3.9%
t 4521
 
3.9%
a 4521
 
3.9%
S 4460
 
3.8%
D 4382
 
3.7%
Other values (25) 58631
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 117272
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10918
 
9.3%
7904
 
6.7%
1 6455
 
5.5%
2 5833
 
5.0%
3 5113
 
4.4%
8 4534
 
3.9%
t 4521
 
3.9%
a 4521
 
3.9%
S 4460
 
3.8%
D 4382
 
3.7%
Other values (25) 58631
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 117272
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10918
 
9.3%
7904
 
6.7%
1 6455
 
5.5%
2 5833
 
5.0%
3 5113
 
4.4%
8 4534
 
3.9%
t 4521
 
3.9%
a 4521
 
3.9%
S 4460
 
3.8%
D 4382
 
3.7%
Other values (25) 58631
50.0%
Distinct6733
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:26.305926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length34
Median length24
Mean length14.795234
Min length2

Characters and Unicode

Total characters152746
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5849 ?
Unique (%)56.7%

Sample

1st row780.34
2nd row4521.5
3rd row1653.78
4th row16007.06
5th row45450.08
ValueCountFrequency (%)
see 2445
 
11.5%
freight 1442
 
6.8%
in 1442
 
6.8%
commodity 1442
 
6.8%
cost 1442
 
6.8%
included 1442
 
6.8%
invoiced 239
 
1.1%
separately 239
 
1.1%
9736.1 36
 
0.2%
6147.18 27
 
0.1%
Other values (8028) 11025
52.0%
2024-12-30T16:46:27.234732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10897
 
7.1%
e 8491
 
5.6%
1 8391
 
5.5%
2 7344
 
4.8%
3 6673
 
4.4%
8 6162
 
4.0%
. 5734
 
3.8%
4 5646
 
3.7%
5 5346
 
3.5%
6 5136
 
3.4%
Other values (32) 82926
54.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 152746
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10897
 
7.1%
e 8491
 
5.6%
1 8391
 
5.5%
2 7344
 
4.8%
3 6673
 
4.4%
8 6162
 
4.0%
. 5734
 
3.8%
4 5646
 
3.7%
5 5346
 
3.5%
6 5136
 
3.4%
Other values (32) 82926
54.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 152746
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10897
 
7.1%
e 8491
 
5.6%
1 8391
 
5.5%
2 7344
 
4.8%
3 6673
 
4.4%
8 6162
 
4.0%
. 5734
 
3.8%
4 5646
 
3.7%
5 5346
 
3.5%
6 5136
 
3.4%
Other values (32) 82926
54.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 152746
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10897
 
7.1%
e 8491
 
5.6%
1 8391
 
5.5%
2 7344
 
4.8%
3 6673
 
4.4%
8 6162
 
4.0%
. 5734
 
3.8%
4 5646
 
3.7%
5 5346
 
3.5%
6 5136
 
3.4%
Other values (32) 82926
54.3%

line item insurance (usd)
Real number (ℝ)

High correlation  Missing 

Distinct6722
Distinct (%)67.0%
Missing287
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean240.11763
Minimum0
Maximum7708.44
Zeros54
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size80.8 KiB
2024-12-30T16:46:27.587157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q16.51
median47.04
Q3252.4
95-th percentile1082.032
Maximum7708.44
Range7708.44
Interquartile range (IQR)245.89

Descriptive statistics

Standard deviation500.19057
Coefficient of variation (CV)2.0831064
Kurtosis34.911215
Mean240.11763
Median Absolute Deviation (MAD)46.27
Skewness4.8271624
Sum2410060.6
Variance250190.6
MonotonicityNot monotonic
2024-12-30T16:46:27.969693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
 
0.5%
0.02 37
 
0.4%
0.07 33
 
0.3%
0.05 30
 
0.3%
0.06 30
 
0.3%
0.01 26
 
0.3%
0.03 23
 
0.2%
0.09 21
 
0.2%
0.08 20
 
0.2%
0.49 18
 
0.2%
Other values (6712) 9745
94.4%
(Missing) 287
 
2.8%
ValueCountFrequency (%)
0 54
0.5%
0.01 26
0.3%
0.02 37
0.4%
0.03 23
0.2%
0.04 14
 
0.1%
0.05 30
0.3%
0.06 30
0.3%
0.07 33
0.3%
0.08 20
 
0.2%
0.09 21
 
0.2%
ValueCountFrequency (%)
7708.44 1
< 0.1%
7005.49 1
< 0.1%
5930.22 1
< 0.1%
5573.31 1
< 0.1%
5479.13 1
< 0.1%
5284.04 1
< 0.1%
5230.81 1
< 0.1%
5162.29 1
< 0.1%
5145 2
< 0.1%
5098.1 1
< 0.1%

Interactions

2024-12-30T16:45:51.823774image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:39.384710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:41.128157image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:43.000301image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:45.044647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:46.900066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:49.335896image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:52.089259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:39.611112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:41.379636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:43.284832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:45.308656image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:47.171658image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:49.683449image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:52.339096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:39.827564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:41.607313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:43.512721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:45.531695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:47.518421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:50.086622image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:52.604334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:40.092228image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:41.875564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:43.927379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:45.767267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:47.815994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:50.534646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:52.857590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:40.325790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:42.215475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:44.245078image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:46.100394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:48.254910image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:50.812883image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:53.156852image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:40.559453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:42.452460image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:44.487715image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:46.422050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:48.537118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:51.074953image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:53.425454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:40.774966image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:42.678670image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:44.722968image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:46.643542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:48.859725image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-30T16:45:51.378521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-30T16:46:28.332543image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
idunit of measure (per pack)line item quantityline item valuepack priceunit priceline item insurance (usd)
id1.000-0.0830.1920.136-0.169-0.0730.104
unit of measure (per pack)-0.0831.000-0.151-0.1290.097-0.103-0.132
line item quantity0.192-0.1511.0000.839-0.134-0.0520.799
line item value0.136-0.1290.8391.000-0.019-0.0200.961
pack price-0.1690.097-0.134-0.0191.0000.250-0.015
unit price-0.073-0.103-0.052-0.0200.2501.000-0.021
line item insurance (usd)0.104-0.1320.7990.961-0.015-0.0211.000
2024-12-30T16:46:28.715904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
idunit of measure (per pack)line item quantityline item valuepack priceunit priceline item insurance (usd)
id1.000-0.0570.2650.143-0.280-0.2240.124
unit of measure (per pack)-0.0571.000-0.186-0.1400.054-0.387-0.134
line item quantity0.265-0.1861.0000.877-0.330-0.2000.874
line item value0.143-0.1400.8771.0000.1310.2000.995
pack price-0.2800.054-0.3300.1311.0000.8590.132
unit price-0.224-0.387-0.2000.2000.8591.0000.200
line item insurance (usd)0.124-0.1340.8740.9950.1320.2001.000
2024-12-30T16:46:29.231796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
brandcountrydosage formfirst line designationfulfill viaidline item insurance (usd)line item quantityline item valuemanaged bypack priceproduct groupshipment modesub classificationunit of measure (per pack)unit pricevendor inco term
brand1.0000.1990.5150.2450.5070.2060.0230.0000.0000.1630.7960.7810.2160.6470.5870.9110.443
country0.1991.0000.1940.3210.6100.2050.0910.0620.0730.0980.1810.3130.5480.3100.1540.1210.441
dosage form0.5150.1941.0000.2300.4760.1710.0930.0810.0890.0190.0950.6530.2390.8250.5460.1260.288
first line designation0.2450.3210.2301.0000.0990.1100.0720.0320.0500.0990.0660.1920.2410.2070.0360.0500.309
fulfill via0.5070.6100.4760.0991.0000.8840.1180.1370.1280.0780.0810.3850.3800.3940.0580.0781.000
id0.2060.2050.1710.1100.8841.0000.1240.2650.1430.067-0.2800.1870.2610.170-0.057-0.2240.338
line item insurance (usd)0.0230.0910.0930.0720.1180.1241.0000.8740.9950.0000.1320.0230.1490.073-0.1340.2000.040
line item quantity0.0000.0620.0810.0320.1370.2650.8741.0000.8770.000-0.3300.0620.1970.088-0.186-0.2000.054
line item value0.0000.0730.0890.0500.1280.1430.9950.8771.0000.0000.1310.0180.1350.062-0.1400.2000.042
managed by0.1630.0980.0190.0990.0780.0670.0000.0000.0001.0000.0750.0180.0680.0180.0000.0000.105
pack price0.7960.1810.0950.0660.081-0.2800.132-0.3300.1310.0751.0000.0940.0390.0990.0540.8590.069
product group0.7810.3130.6530.1920.3850.1870.0230.0620.0180.0180.0941.0000.1920.8660.6150.1180.322
shipment mode0.2160.5480.2390.2410.3800.2610.1490.1970.1350.0680.0390.1921.0000.2000.0580.0580.380
sub classification0.6470.3100.8250.2070.3940.1700.0730.0880.0620.0180.0990.8660.2001.0000.5730.1300.284
unit of measure (per pack)0.5870.1540.5460.0360.058-0.057-0.134-0.186-0.1400.0000.0540.6150.0580.5731.000-0.3870.159
unit price0.9110.1210.1260.0500.078-0.2240.200-0.2000.2000.0000.8590.1180.0580.130-0.3871.0000.072
vendor inco term0.4430.4410.2880.3091.0000.3380.0400.0540.0420.1050.0690.3220.3800.2840.1590.0721.000

Missing values

2024-12-30T16:45:53.917259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-30T16:45:54.934217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-30T16:45:55.564580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idproject codepq #po / so #asn/dn #countrymanaged byfulfill viavendor inco termshipment modepq first sent to client datepo sent to vendor datescheduled delivery datedelivered to client datedelivery recorded dateproduct groupsub classificationvendoritem descriptionmolecule/test typebranddosagedosage formunit of measure (per pack)line item quantityline item valuepack priceunit pricemanufacturing sitefirst line designationweight (kilograms)freight cost (usd)line item insurance (usd)
01100-CI-T01Pre-PQ ProcessSCMS-4ASN-8Côte d'IvoirePMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured2-Jun-062-Jun-062-Jun-06HRDTHIV testRANBAXY Fine Chemicals LTD.HIV, Reveal G3 Rapid HIV-1 Antibody Test, 30 TestsHIV, Reveal G3 Rapid HIV-1 Antibody TestRevealNaNTest kit3019551.0029.000.97Ranbaxy Fine Chemicals LTDTrue13780.34NaN
13108-VN-T01Pre-PQ ProcessSCMS-13ASN-85VietnamPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured14-Nov-0614-Nov-0614-Nov-06ARVPediatricAurobindo Pharma LimitedNevirapine 10mg/ml, oral suspension, Bottle, 240 mlNevirapineGeneric10mg/mlOral suspension24010006200.006.200.03Aurobindo Unit III, IndiaTrue3584521.5NaN
24100-CI-T01Pre-PQ ProcessSCMS-20ASN-14Côte d'IvoirePMO - USDirect DropFCAAirPre-PQ ProcessDate Not Captured27-Aug-0627-Aug-0627-Aug-06HRDTHIV testAbbott GmbH & Co. KGHIV 1/2, Determine Complete HIV Kit, 100 TestsHIV 1/2, Determine Complete HIV KitDetermineNaNTest kit10050040000.0080.000.80ABBVIE GmbH & Co.KG WiesbadenTrue1711653.78NaN
315108-VN-T01Pre-PQ ProcessSCMS-78ASN-50VietnamPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured1-Sep-061-Sep-061-Sep-06ARVAdultSUN PHARMACEUTICAL INDUSTRIES LTD (RANBAXY LABORATORIES LIMITED)Lamivudine 150mg, tablets, 60 TabsLamivudineGeneric150mgTablet6031920127360.803.990.07Ranbaxy, Paonta Shahib, IndiaTrue185516007.06NaN
416108-VN-T01Pre-PQ ProcessSCMS-81ASN-55VietnamPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured11-Aug-0611-Aug-0611-Aug-06ARVAdultAurobindo Pharma LimitedStavudine 30mg, capsules, 60 CapsStavudineGeneric30mgCapsule6038000121600.003.200.05Aurobindo Unit III, IndiaTrue759045450.08NaN
523112-NG-T01Pre-PQ ProcessSCMS-87ASN-57NigeriaPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured28-Sep-0628-Sep-0628-Sep-06ARVPediatricAurobindo Pharma LimitedZidovudine 10mg/ml, oral solution, Bottle, 240 mlZidovudineGeneric10mg/mlOral solution2404162225.605.350.02Aurobindo Unit III, IndiaTrue5045920.42NaN
644110-ZM-T01Pre-PQ ProcessSCMS-139ASN-130ZambiaPMO - USDirect DropDDUAirPre-PQ ProcessDate Not Captured8-Jan-078-Jan-078-Jan-07ARVPediatricMERCK SHARP & DOHME IDEA GMBH (FORMALLY MERCK SHARP & DOHME B.V.)Efavirenz 200mg [Stocrin/Sustiva], capsule, 90 CapsEfavirenzStocrin/Sustiva200mgCapsule901354374.0032.400.36MSD South Granville AustraliaTrue328Freight Included in Commodity CostNaN
745109-TZ-T01Pre-PQ ProcessSCMS-140ASN-94TanzaniaPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured24-Nov-0624-Nov-0624-Nov-06ARVAdultAurobindo Pharma LimitedNevirapine 200mg, tablets, 60 TabsNevirapineGeneric200mgTablet601666760834.553.650.06Aurobindo Unit III, IndiaTrue14786212.41NaN
846112-NG-T01Pre-PQ ProcessSCMS-156ASN-93NigeriaPMO - USDirect DropEXWAirPre-PQ ProcessDate Not Captured7-Dec-067-Dec-067-Dec-06ARVAdultAurobindo Pharma LimitedStavudine 30mg, capsules, 60 CapsStavudineGeneric30mgCapsule60273532.351.950.03Aurobindo Unit III, IndiaFalseSee ASN-93 (ID#:1281)See ASN-93 (ID#:1281)NaN
947110-ZM-T01Pre-PQ ProcessSCMS-165ASN-199ZambiaPMO - USDirect DropCIPAirPre-PQ Process11/13/200630-Jan-0730-Jan-0730-Jan-07ARVAdultABBVIE LOGISTICS (FORMERLY ABBOTT LOGISTICS BV)Lopinavir/Ritonavir 200/50mg [Aluvia], tablets, 120 TabsLopinavir/RitonavirAluvia200/50mgTablet1202800115080.0041.100.34ABBVIE (Abbott) St. P'burg USATrue643Freight Included in Commodity CostNaN
idproject codepq #po / so #asn/dn #countrymanaged byfulfill viavendor inco termshipment modepq first sent to client datepo sent to vendor datescheduled delivery datedelivered to client datedelivery recorded dateproduct groupsub classificationvendoritem descriptionmolecule/test typebranddosagedosage formunit of measure (per pack)line item quantityline item valuepack priceunit pricemanufacturing sitefirst line designationweight (kilograms)freight cost (usd)line item insurance (usd)
1031486813151-NG-T30FPQ-14989SO-51422DN-4274NigeriaPMO - USFrom RDCN/A - From RDCAir Charter9/19/2014N/A - From RDC30-Jun-1515-May-1522-May-15ARVPediatricSCMS from RDCLamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsLamivudine/Nevirapine/ZidovudineGeneric30/50/60mgChewable/dispersible tablet - FDC601034037224.003.600.06Mylan (formerly Matrix) NashikFalseSee DN-4274 (ID#:84472)See DN-4274 (ID#:84472)38.27
1031586814151-NG-T30FPQ-14989SO-51424DN-4276NigeriaPMO - USFrom RDCN/A - From RDCAir Charter9/19/2014N/A - From RDC30-Jun-1515-May-1522-May-15ARVAdultSCMS from RDCLopinavir/Ritonavir 200/50mg [Aluvia], tablets, 120 TabsLopinavir/RitonavirAluvia200/50mgTablet120700001304800.0018.640.16ABBVIE Ludwigshafen GermanyTrue15198261801341.33
1031686815151-NG-T30FPQ-16313SO-51420DN-4279NigeriaPMO - USFrom RDCN/A - From RDCAir Charter5/4/2015N/A - From RDC2-Jun-1515-May-1522-May-15ARVAdultSCMS from RDCLamivudine/Zidovudine 150/300mg, tablets, 60 TabsLamivudine/ZidovudineGeneric150/300mgTablet - FDC601500097800.006.520.11Aurobindo Unit III, IndiaTrue15473410115.11
1031786816151-NG-T30FPQ-16313SO-51440DN-4282NigeriaPMO - USFrom RDCN/A - From RDCAir5/4/2015N/A - From RDC30-Jun-1522-Jun-1529-Jun-15ARVAdultSCMS from RDCEfavirenz 600mg, tablets, 30 TabsEfavirenzGeneric600mgTablet30672420978.883.120.10Strides, Bangalore, India.FalseSee DN-4282 (ID#:83919)See DN-4282 (ID#:83919)24.69
1031886817103-ZW-T30FPQ-15197SO-50020DN-4307ZimbabwePMO - USFrom RDCN/A - From RDCTruck10/16/2014N/A - From RDC31-Jul-1515-Jul-1520-Jul-15ARVPediatricSCMS from RDCLamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsLamivudine/Nevirapine/ZidovudineGeneric30/50/60mgChewable/dispersible tablet - FDC60205243738874.803.600.06Cipla, Goa, IndiaFalseSee DN-4307 (ID#:83920)See DN-4307 (ID#:83920)869.66
1031986818103-ZW-T30FPQ-15197SO-50020DN-4307ZimbabwePMO - USFrom RDCN/A - From RDCTruck10/16/2014N/A - From RDC31-Jul-1515-Jul-1520-Jul-15ARVPediatricSCMS from RDCLamivudine/Nevirapine/Zidovudine 30/50/60mg, dispersible tablets, 60 TabsLamivudine/Nevirapine/ZidovudineGeneric30/50/60mgChewable/dispersible tablet - FDC60166571599655.603.600.06Mylan, H-12 & H-13, IndiaFalseSee DN-4307 (ID#:83920)See DN-4307 (ID#:83920)705.79
1032086819104-CI-T30FPQ-15259SO-50102DN-4313Côte d'IvoirePMO - USFrom RDCN/A - From RDCTruck10/24/2014N/A - From RDC31-Jul-156-Aug-157-Aug-15ARVAdultSCMS from RDCLamivudine/Zidovudine 150/300mg, tablets, 60 TabsLamivudine/ZidovudineGeneric150/300mgTablet - FDC6021072137389.446.520.11Hetero Unit III Hyderabad INFalseSee DN-4313 (ID#:83921)See DN-4313 (ID#:83921)161.71
1032186821110-ZM-T30FPQ-14784SO-49600DN-4316ZambiaPMO - USFrom RDCN/A - From RDCTruck8/12/2014N/A - From RDC31-Aug-1525-Aug-153-Sep-15ARVAdultSCMS from RDCEfavirenz/Lamivudine/Tenofovir Disoproxil Fumarate 600/300/300mg, tablets, 30 TabsEfavirenz/Lamivudine/Tenofovir Disoproxil FumarateGeneric600/300/300mgTablet - FDC305145265140114.749.990.33Cipla Ltd A-42 MIDC Mahar. INFalseWeight Captured SeparatelyFreight Included in Commodity Cost5284.04
1032286822200-ZW-T30FPQ-16523SO-51680DN-4334ZimbabwePMO - USFrom RDCN/A - From RDCTruck7/1/2015N/A - From RDC9-Sep-154-Aug-1511-Aug-15ARVAdultSCMS from RDCLamivudine/Zidovudine 150/300mg, tablets, 60 TabsLamivudine/ZidovudineGeneric150/300mgTablet - FDC6017465113871.806.520.11Mylan (formerly Matrix) NashikTrue1392Freight Included in Commodity Cost134.03
1032386823103-ZW-T30FPQ-15197SO-50022DN-4336ZimbabwePMO - USFrom RDCN/A - From RDCTruck10/16/2014N/A - From RDC31-Aug-154-Aug-1511-Aug-15ARVPediatricSCMS from RDCLamivudine/Zidovudine 30/60mg, dispersible tablets, 60 TabsLamivudine/ZidovudineGeneric30/60mgChewable/dispersible tablet - FDC603663972911.611.990.03Cipla, Goa, IndiaFalseWeight Captured SeparatelyFreight Included in Commodity Cost85.82